A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The int...A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The interconnection probability of the two targets is calculated,the weighted value is processed and the target tracks are obtained.The simulation results show that JPDA algorithm achieves higher tracking accuracy and provides a basis for more targets tracking.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
文摘A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The interconnection probability of the two targets is calculated,the weighted value is processed and the target tracks are obtained.The simulation results show that JPDA algorithm achieves higher tracking accuracy and provides a basis for more targets tracking.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
